A New Particle Swarm Optimization Algorithm and Its Convergence Analysis

This paper modified the structure of the original PSO algorithm. It proposes that the particles´ position have relationship with the one particle´s and the whole swarm´s perceive extent in the processing of this time and last time, and presents the inertial weight based on simulated annealing temperature. So a new Particle Swarm Optimization algorithm (NPSO) is proposed. It can not improve the one particle´s and the whole swarm´s perceivable extent and improve the searching efficient but also increase variety of particles and overcome the defect of sinking the local optimal efficiently. At the same time we give the convergence condition of this new algorithm. The algorithm of PSO and NPSO and LPSO are tested with four well-known benchmark functions. The experiments show that the convergence speed of NPSO is significantly superior to PSO and LPSO. The convergence accuracy is increased.